Reinforcement Learning#

Reinforcement learning is a subfield of artificial intelligence and machine learning that focuses on teaching agents to make decisions in an environment by performing actions and receiving rewards. The goal of reinforcement learning is to learn a policy that maximizes the cumulative reward over time. The policy is a mapping from states to actions, and it represents the decision-making process of the agent. The interaction between the agent and the environment creates a sequence of states, actions, and rewards, known as a trajectory. Reinforcement learning algorithms use this trajectory to estimate the value of different states and actions and to update the policy accordingly. In this chapter, we will explore the basics of reinforcement learning and the various algorithms that are used to solve reinforcement learning problems.

Where to Learn More#

I’ve covered Reinforcement Learning in-depth in the following courses:

Artificial Intelligence: Reinforcement Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

Cutting-Edge AI: Deep Reinforcement Learning in Python

And we apply Reinforcement Learning in the following courses:

Tensorflow 2.0: Deep Learning and Artificial Intelligence

PyTorch: Deep Learning and Artificial Intelligence

Financial Engineering and Artificial Intelligence in Python